Table 2.

The influence of geography, age, gender and health facility type on the choice of microscopy or RDT for malaria diagnosis in the Solomon Islands during the years 2017–2019 for all Plasmodium species, determined through generalized linear models (GLMs)

Explanatory factors	All Plasmodium spp. infections
	χ2(a)	dfb	P valuec
National level model
Age	863.7	95	< 2e-16*
Gender	2.5	2	0.2876
HF type	14534.2	4	< 2e-16*
Year	789.7	1	< 2e-16*
Provincial level model
Province	11847.4	8	< 2e-16*
Age	461.7	96	< 2e-16*
Gender	0.5	20	0.8854
HF type	13912.5	4	< 2e-16*
Year	934.0	1	< 2e-16*
Health zone level model
Health zone	30812.2	43	< 2e-16*
Age	341.6	96	< 2e-16*
Gender	5.9	2	0.0527
HF type	15030.6	4	< 2e-16*
Year	1342.0	1	< 2e-16*
Health facility level model
Health facility	84959	330	< 2e-16*
Age	263	96	< 2e-16*
Gender	26	2	1.907e-06*
Year	1878	1	< 2e-16*

^aχ², Chi-square values that measure the difference between observed and expected value

^bdf, degrees of freedom

^c*P < 0.05, indicates significant influence on the choice of diagnostic test type